基于小波分析和神经网络的井下电缆故障测距方法

Fault Location Method of Underground Cable Based on Wavelet Analysis and Neural Network

  • 摘要: 针对现有的井下电缆故障测距方法存在可靠性差、精度低的问题,介绍了一种基于小波分析理论和神经网络的井下电缆故障测距方法,并比较了BP神经网络和RBF神经网络用于该方法的测距性能。该故障测距方法采用3次B样条半正交小波对暂态零序电流信号进行小波变换,得到特定频带内的暂态零序电流模极大值,并将该模极大值作为神经网络的输入信号,根据模极大值与故障点位置的映射关系实现故障定位。仿真结果表明,该故障测距方法能够较好地进行井下电缆故障测距,且RBF神经网络的测距误差及训练速度均优于BP神经网络。

     

    Abstract: In order to solve problems of poor reliability and accuracy of existing fault location methods of underground cable, the paper introduced a fault location method of underground cable based on wavelet analysis and neural network, and compared performance of BP neural network and RBF neural network used in the method. The method uses 3B-spline semi-orthogonal wavelet to do wavelet transformation for transient-state zero-sequence current so as to get modulus maxima of transient-state zero-sequence current in specific frequency bands. The modulus maxima is taken as inputting signals of neural network, and realizes fault location according to mapping relationship between the modulus maxima and position of fault point. The simulation results showed that the method can realize fault location of underground cable, and the method with RBP neural network is better than BF neural network in location error and network training.

     

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